



import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

import evaluate
import numpy as np
import torch
from datasets import load_dataset
from PIL import Image
from torchvision.transforms import (
    CenterCrop,
    Compose,
    Normalize,
    RandomHorizontalFlip,
    RandomResizedCrop,
    Resize,
    ToTensor,
)

import transformers
from transformers import (
    MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
    AutoConfig,
    AutoImageProcessor,
    AutoModelForImageClassification,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version
a      :  int			     =			logging.getLogger(__name__)

# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version('4.31.0')

require_version('datasets>=1.8.0', 'To fix: pip install -r examples/pytorch/image-classification/requirements.txt')

a      :  Dict			     =			list(MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING.keys())
a      :  List[Any]			     =			tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)







def  lowerCAmelCase_	(lowerCAmelCase__:		str  ):





			"""simple docstring"""


			with open(lowerCAmelCase__		,						"""rb"""  ) as f:
						UpperCAmelCase_:		Optional[Any]							      =     Image.open(lowerCAmelCase__  )
						return im.convert("""RGB"""  )







@dataclass
class 				_a				:
			A      		=	field(
			    default=_lowerCAmelCase ,		metadata={
			        '''help''': '''Name of a dataset from the hub (could be your own, possibly private dataset hosted on the hub).'''
			    } ,		)
			A      		=	field(
			    default=_lowerCAmelCase ,		metadata={'''help''': '''The configuration name of the dataset to use (via the datasets library).'''}					)
			A      		=	field(default=_lowerCAmelCase ,		metadata={'''help''': '''A folder containing the training data.'''}					)
			A      		=	field(default=_lowerCAmelCase ,		metadata={'''help''': '''A folder containing the validation data.'''}					)
			A      		=	field(
			    default=0.15 ,		metadata={'''help''': '''Percent to split off of train for validation.'''}					)
			A      		=	field(
			    default=_lowerCAmelCase ,		metadata={
			        '''help''': (
			            '''For debugging purposes or quicker training, truncate the number of training examples to this '''
			            '''value if set.'''
			        )
			    } ,		)
			A      		=	field(
			    default=_lowerCAmelCase ,		metadata={
			        '''help''': (
			            '''For debugging purposes or quicker training, truncate the number of evaluation examples to this '''
			            '''value if set.'''
			        )
			    } ,		)

			def      __snake_case       (self					)       ->   Optional[Any]:
						if self.dataset_name is None and (self.train_dir is None and self.validation_dir is None):
									raise ValueError(
									    """You must specify either a dataset name from the hub or a train and/or validation directory."""					)







@dataclass
class 				_a				:
			A      		=	field(
			    default='''google/vit-base-patch16-224-in21k''' ,		metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''} ,		)
			A      		=	field(
			    default=_lowerCAmelCase ,		metadata={'''help''': '''If training from scratch, pass a model type from the list: ''' + ''', '''.join(_lowerCAmelCase					)} ,		)
			A      		=	field(
			    default=_lowerCAmelCase ,		metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''}					)
			A      		=	field(
			    default=_lowerCAmelCase ,		metadata={'''help''': '''Where do you want to store the pretrained models downloaded from s3'''}					)
			A      		=	field(
			    default='''main''' ,		metadata={'''help''': '''The specific model version to use (can be a branch name, tag name or commit id).'''} ,		)
			A      		=	field(default=_lowerCAmelCase ,		metadata={'''help''': '''Name or path of preprocessor config.'''}					)
			A      		=	field(
			    default=_lowerCAmelCase ,		metadata={
			        '''help''': (
			            '''Will use the token generated when running `huggingface-cli login` (necessary to use this script '''
			            '''with private models).'''
			        )
			    } ,		)
			A      		=	field(
			    default=_lowerCAmelCase ,		metadata={'''help''': '''Will enable to load a pretrained model whose head dimensions are different.'''} ,		)







def  lowerCAmelCase_	(lowerCAmelCase__:		Optional[Any]  ):





			"""simple docstring"""


			UpperCAmelCase_:		str							      =     torch.stack([example["""pixel_values"""] for example in examples]  )
			UpperCAmelCase_:		Optional[int]							      =     torch.tensor([example["""labels"""] for example in examples]  )
			return {"pixel_values": pixel_values, "labels": labels}







def  lowerCAmelCase_	():





			"""simple docstring"""


			UpperCAmelCase_:		Dict							      =     HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)  )
			if len(sys.argv  ) == 2 and sys.argv[1].endswith(""".json"""  ):
						# If we pass only one argument to the script and it's the path to a json file,
						# let's parse it to get our arguments.
						UpperCAmelCase_      ,							UpperCAmelCase_      ,							UpperCAmelCase_:		Optional[Any]							      =     parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]  )  )
			else:
						UpperCAmelCase_      ,							UpperCAmelCase_      ,							UpperCAmelCase_:		Any							      =     parser.parse_args_into_dataclasses()

			# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
			# information sent is the one passed as arguments along with your Python/PyTorch versions.
			send_example_telemetry("""run_image_classification"""		,						lowerCAmelCase__		,						lowerCAmelCase__  )

			# Setup logging
			logging.basicConfig(
			    format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s"""		,						datefmt="""%m/%d/%Y %H:%M:%S"""		,						handlers=[logging.StreamHandler(sys.stdout  )]		,						)

			if training_args.should_log:
						# The default of training_args.log_level is passive, so we set log level at info here to have that default.
						transformers.utils.logging.set_verbosity_info()

			UpperCAmelCase_:		Optional[int]							      =     training_args.get_process_log_level()
			logger.setLevel(lowerCAmelCase__  )
			transformers.utils.logging.set_verbosity(lowerCAmelCase__  )
			transformers.utils.logging.enable_default_handler()
			transformers.utils.logging.enable_explicit_format()

			# Log on each process the small summary:
			logger.warning(
			    F'Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}'
			    + F'distributed training: {bool(training_args.local_rank != -1  )}, 16-bits training: {training_args.fpaa}'  )
			logger.info(F'Training/evaluation parameters {training_args}'  )

			# Detecting last checkpoint.
			UpperCAmelCase_:		Optional[Any]							      =     None
			if os.path.isdir(training_args.output_dir  ) and training_args.do_train and not training_args.overwrite_output_dir:
						UpperCAmelCase_:		str							      =     get_last_checkpoint(training_args.output_dir  )
						if last_checkpoint is None and len(os.listdir(training_args.output_dir  )  ) > 0:
									raise ValueError(
									    F'Output directory ({training_args.output_dir}) already exists and is not empty. '
									    """Use --overwrite_output_dir to overcome."""  )
						elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
									logger.info(
									    F'Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change '
									    """the `--output_dir` or add `--overwrite_output_dir` to train from scratch."""  )

    # Set seed before initializing model.
			set_seed(training_args.seed  )

			# Initialize our dataset and prepare it for the 'image-classification' task.
			if data_args.dataset_name is not None:
						UpperCAmelCase_:		Any							      =     load_dataset(
						    data_args.dataset_name		,						data_args.dataset_config_name		,						cache_dir=model_args.cache_dir		,						task="""image-classification"""		,						use_auth_token=True if model_args.use_auth_token else None		,						)
			else:
						UpperCAmelCase_:		str							      =     {}
						if data_args.train_dir is not None:
									UpperCAmelCase_:		int							      =     os.path.join(data_args.train_dir		,						"""**"""  )
						if data_args.validation_dir is not None:
									UpperCAmelCase_:		Dict							      =     os.path.join(data_args.validation_dir		,						"""**"""  )
						UpperCAmelCase_:		Optional[Any]							      =     load_dataset(
						    """imagefolder"""		,						data_files=lowerCAmelCase__		,						cache_dir=model_args.cache_dir		,						task="""image-classification"""		,						)

			# If we don't have a validation split, split off a percentage of train as validation.
			UpperCAmelCase_:		int							      =     None if """validation""" in dataset.keys() else data_args.train_val_split
			if isinstance(data_args.train_val_split		,						lowerCAmelCase__  ) and data_args.train_val_split > 0.0:
						UpperCAmelCase_:		List[Any]							      =     dataset["""train"""].train_test_split(data_args.train_val_split  )
						UpperCAmelCase_:		Any							      =     split["""train"""]
						UpperCAmelCase_:		int							      =     split["""test"""]

			# Prepare label mappings.
			# We'll include these in the model's config to get human readable labels in the Inference API.
			UpperCAmelCase_:		Union[str, Any]							      =     dataset["""train"""].features["""labels"""].names
			UpperCAmelCase_      ,							UpperCAmelCase_:		Union[str, Any]							      =     {}, {}
			for i, label in enumerate(lowerCAmelCase__  ):
						UpperCAmelCase_:		str							      =     str(lowerCAmelCase__  )
						UpperCAmelCase_:		Optional[int]							      =     label

			# Load the accuracy metric from the datasets package
			UpperCAmelCase_:		str							      =     evaluate.load("""accuracy"""  )

			# Define our compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a
			# predictions and label_ids field) and has to return a dictionary string to float.
			def compute_metrics(lowerCAmelCase__:		Any  ):
						return metric.compute(predictions=np.argmax(p.predictions		,						axis=1  )		,						references=p.label_ids  )

			UpperCAmelCase_:		int							      =     AutoConfig.from_pretrained(
			    model_args.config_name or model_args.model_name_or_path		,						num_labels=len(lowerCAmelCase__  )		,						labelaid=lowerCAmelCase__		,						idalabel=lowerCAmelCase__		,						finetuning_task="""image-classification"""		,						cache_dir=model_args.cache_dir		,						revision=model_args.model_revision		,						use_auth_token=True if model_args.use_auth_token else None		,						)
			UpperCAmelCase_:		Union[str, Any]							      =     AutoModelForImageClassification.from_pretrained(
			    model_args.model_name_or_path		,						from_tf=bool(""".ckpt""" in model_args.model_name_or_path  )		,						config=lowerCAmelCase__		,						cache_dir=model_args.cache_dir		,						revision=model_args.model_revision		,						use_auth_token=True if model_args.use_auth_token else None		,						ignore_mismatched_sizes=model_args.ignore_mismatched_sizes		,						)
			UpperCAmelCase_:		Optional[Any]							      =     AutoImageProcessor.from_pretrained(
			    model_args.image_processor_name or model_args.model_name_or_path		,						cache_dir=model_args.cache_dir		,						revision=model_args.model_revision		,						use_auth_token=True if model_args.use_auth_token else None		,						)

			# Define torchvision transforms to be applied to each image.
			if "shortest_edge" in image_processor.size:
						UpperCAmelCase_:		List[str]							      =     image_processor.size["""shortest_edge"""]
			else:
						UpperCAmelCase_:		int							      =     (image_processor.size["""height"""], image_processor.size["""width"""])
			UpperCAmelCase_:		str							      =     Normalize(mean=image_processor.image_mean		,						std=image_processor.image_std  )
			UpperCAmelCase_:		Union[str, Any]							      =     Compose(
			    [
			        RandomResizedCrop(lowerCAmelCase__  ),
			        RandomHorizontalFlip(),
			        ToTensor(),
			        normalize,
			    ]  )
			UpperCAmelCase_:		Any							      =     Compose(
			    [
			        Resize(lowerCAmelCase__  ),
			        CenterCrop(lowerCAmelCase__  ),
			        ToTensor(),
			        normalize,
			    ]  )

			def train_transforms(lowerCAmelCase__:		str  ):
						UpperCAmelCase_:		Optional[int]							      =     [
						    _train_transforms(pil_img.convert("""RGB"""  )  ) for pil_img in example_batch["""image"""]
						]
						return example_batch

			def val_transforms(lowerCAmelCase__:		Optional[int]  ):
						UpperCAmelCase_:		Dict							      =     [_val_transforms(pil_img.convert("""RGB"""  )  ) for pil_img in example_batch["""image"""]]
						return example_batch

			if training_args.do_train:
						if "train" not in dataset:
									raise ValueError("""--do_train requires a train dataset"""  )
						if data_args.max_train_samples is not None:
									UpperCAmelCase_:		Optional[Any]							      =     (
									    dataset["""train"""].shuffle(seed=training_args.seed  ).select(range(data_args.max_train_samples  )  )
									)
						# Set the training transforms
						dataset["train"].set_transform(lowerCAmelCase__  )

			if training_args.do_eval:
						if "validation" not in dataset:
									raise ValueError("""--do_eval requires a validation dataset"""  )
						if data_args.max_eval_samples is not None:
									UpperCAmelCase_:		int							      =     (
									    dataset["""validation"""].shuffle(seed=training_args.seed  ).select(range(data_args.max_eval_samples  )  )
									)
						# Set the validation transforms
						dataset["validation"].set_transform(lowerCAmelCase__  )

			# Initalize our trainer
			UpperCAmelCase_:		str							      =     Trainer(
			    model=lowerCAmelCase__		,						args=lowerCAmelCase__		,						train_dataset=dataset["""train"""] if training_args.do_train else None		,						eval_dataset=dataset["""validation"""] if training_args.do_eval else None		,						compute_metrics=lowerCAmelCase__		,						tokenizer=lowerCAmelCase__		,						data_collator=lowerCAmelCase__		,						)

			# Training
			if training_args.do_train:
						UpperCAmelCase_:		int							      =     None
						if training_args.resume_from_checkpoint is not None:
									UpperCAmelCase_:		List[Any]							      =     training_args.resume_from_checkpoint
						elif last_checkpoint is not None:
									UpperCAmelCase_:		List[str]							      =     last_checkpoint
						UpperCAmelCase_:		Any							      =     trainer.train(resume_from_checkpoint=lowerCAmelCase__  )
						trainer.save_model()
						trainer.log_metrics("""train"""		,						train_result.metrics  )
						trainer.save_metrics("""train"""		,						train_result.metrics  )
						trainer.save_state()

			# Evaluation
			if training_args.do_eval:
						UpperCAmelCase_:		str							      =     trainer.evaluate()
						trainer.log_metrics("""eval"""		,						lowerCAmelCase__  )
						trainer.save_metrics("""eval"""		,						lowerCAmelCase__  )

			# Write model card and (optionally) push to hub
			UpperCAmelCase_:		Any							      =     {
			    """finetuned_from""": model_args.model_name_or_path,
			    """tasks""": """image-classification""",
			    """dataset""": data_args.dataset_name,
			    """tags""": ["""image-classification""", """vision"""],
			}
			if training_args.push_to_hub:
						trainer.push_to_hub(**lowerCAmelCase__  )
			else:
						trainer.create_model_card(**lowerCAmelCase__  )


if __name__ == "__main__":
	main()











import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm import create_model
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform

from transformers import BitConfig, BitForImageClassification, BitImageProcessor
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
a      :  int			     =			logging.get_logger(__name__)







def  lowerCAmelCase_	(lowerCAmelCase__:		Optional[Any]  ):





			"""simple docstring"""


			UpperCAmelCase_:		str							      =     """huggingface/label-files"""
			UpperCAmelCase_:		Optional[int]							      =     """imagenet-1k-id2label.json"""
			UpperCAmelCase_:		Union[str, Any]							      =     json.load(open(hf_hub_download(lowerCAmelCase__		,						lowerCAmelCase__		,						repo_type="""dataset"""  )		,						"""r"""  )  )
			UpperCAmelCase_:		Optional[Any]							      =     {int(lowerCAmelCase__  ): v for k, v in idalabel.items()}
			UpperCAmelCase_:		Tuple							      =     {v: k for k, v in idalabel.items()}

			UpperCAmelCase_:		int							      =     """std_conv""" if """bit""" in model_name else False

			# note that when using BiT as backbone for ViT-hybrid checkpoints,
			# one needs to additionally set config.layer_type = "bottleneck", config.stem_type = "same",
			# config.conv_layer = "std_conv_same"
			UpperCAmelCase_:		Any							      =     BitConfig(
			    conv_layer=lowerCAmelCase__		,						num_labels=1_0_0_0		,						idalabel=lowerCAmelCase__		,						labelaid=lowerCAmelCase__		,						)

			return config







def  lowerCAmelCase_	(lowerCAmelCase__:		List[Any]  ):





			"""simple docstring"""


			if "stem.conv" in name:
						UpperCAmelCase_:		Dict							      =     name.replace("""stem.conv"""		,						"""bit.embedder.convolution"""  )
			if "blocks" in name:
						UpperCAmelCase_:		Tuple							      =     name.replace("""blocks"""		,						"""layers"""  )
			if "head.fc" in name:
						UpperCAmelCase_:		int							      =     name.replace("""head.fc"""		,						"""classifier.1"""  )
			if name.startswith("""norm"""  ):
						UpperCAmelCase_:		List[Any]							      =     """bit.""" + name
			if "bit" not in name and "classifier" not in name:
						UpperCAmelCase_:		str							      =     """bit.encoder.""" + name

			return name







def  lowerCAmelCase_	():





			"""simple docstring"""


			UpperCAmelCase_:		Any							      =     """http://images.cocodataset.org/val2017/000000039769.jpg"""
			UpperCAmelCase_:		Optional[int]							      =     Image.open(requests.get(lowerCAmelCase__		,						stream=lowerCAmelCase__  ).raw  )
			return im







@torch.no_grad()
def  lowerCAmelCase_	(lowerCAmelCase__:		Tuple		,						lowerCAmelCase__:		Optional[Any]		,						lowerCAmelCase__:		str=False  ):





			"""simple docstring"""


			UpperCAmelCase_:		List[str]							      =     get_config(lowerCAmelCase__  )

			# load original model from timm
			UpperCAmelCase_:		str							      =     create_model(lowerCAmelCase__		,						pretrained=lowerCAmelCase__  )
			timm_model.eval()

			# load state_dict of original model
			UpperCAmelCase_:		Optional[Any]							      =     timm_model.state_dict()
			for key in state_dict.copy().keys():
						UpperCAmelCase_:		Any							      =     state_dict.pop(lowerCAmelCase__  )
						UpperCAmelCase_:		Optional[Any]							      =     val.squeeze() if """head""" in key else val

			# load HuggingFace model
			UpperCAmelCase_:		Union[str, Any]							      =     BitForImageClassification(lowerCAmelCase__  )
			model.eval()
			model.load_state_dict(lowerCAmelCase__  )

			# create image processor
			UpperCAmelCase_:		Any							      =     create_transform(**resolve_data_config({}		,						model=lowerCAmelCase__  )  )
			UpperCAmelCase_:		List[Any]							      =     transform.transforms

			UpperCAmelCase_:		Tuple							      =     {
			    """bilinear""": PILImageResampling.BILINEAR,
			    """bicubic""": PILImageResampling.BICUBIC,
			    """nearest""": PILImageResampling.NEAREST,
			}

			UpperCAmelCase_:		int							      =     BitImageProcessor(
			    do_resize=lowerCAmelCase__		,						size={"""shortest_edge""": timm_transforms[0].size}		,						resample=pillow_resamplings[timm_transforms[0].interpolation.value]		,						do_center_crop=lowerCAmelCase__		,						crop_size={"""height""": timm_transforms[1].size[0], """width""": timm_transforms[1].size[1]}		,						do_normalize=lowerCAmelCase__		,						image_mean=timm_transforms[-1].mean.tolist()		,						image_std=timm_transforms[-1].std.tolist()		,						)

			UpperCAmelCase_:		Optional[Any]							      =     prepare_img()
			UpperCAmelCase_:		Tuple							      =     transform(lowerCAmelCase__  ).unsqueeze(0  )
			UpperCAmelCase_:		Dict							      =     processor(lowerCAmelCase__		,						return_tensors="""pt"""  ).pixel_values

			# verify pixel values
			assert torch.allclose(lowerCAmelCase__		,						lowerCAmelCase__  )

			# verify logits
			with torch.no_grad():
						UpperCAmelCase_:		Dict							      =     model(lowerCAmelCase__  )
						UpperCAmelCase_:		str							      =     outputs.logits

			print("""Logits:"""		,						logits[0, :3]  )
			print("""Predicted class:"""		,						model.config.idalabel[logits.argmax(-1  ).item()]  )
			UpperCAmelCase_:		List[str]							      =     timm_model(lowerCAmelCase__  )
			assert timm_logits.shape == outputs.logits.shape
			assert torch.allclose(lowerCAmelCase__		,						outputs.logits		,						atol=1e-3  )
			print("""Looks ok!"""  )

			if pytorch_dump_folder_path is not None:
						Path(lowerCAmelCase__  ).mkdir(exist_ok=lowerCAmelCase__  )
						print(F'Saving model {model_name} and processor to {pytorch_dump_folder_path}'  )
						model.save_pretrained(lowerCAmelCase__  )
						processor.save_pretrained(lowerCAmelCase__  )

			if push_to_hub:
						print(F'Pushing model {model_name} and processor to the hub'  )
						model.push_to_hub(F'ybelkada/{model_name}'  )
						processor.push_to_hub(F'ybelkada/{model_name}'  )


if __name__ == "__main__":
	a      :  Dict			     =			argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    '--model_name',
	    default='resnetv2_50x1_bitm',
	    type=str,
	    help='Name of the BiT timm model you\'d like to convert.',
	)
	parser.add_argument(
	    '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
	)
	parser.add_argument(
	    '--push_to_hub',
	    action='store_true',
	    help='Whether to push the model to the hub.',
	)

	a      :  Tuple			     =			parser.parse_args()
	convert_bit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)






